Roche
Intern in Machine Learning for Antibody-Antigen Complex Scoring(m/w/x)
Full-timeInternshipOn-site
Basel
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RORoche
Intern in structure-based foundation modelling(m/w/x)
Basel
Full-timeInternshipOn-site
AI/ML
Nejo AI Summary
Generating large-scale synthetic biomolecular structure datasets for foundation models. Master’s student with ML and geometric representation learning experience required. Collaboration in international, cross-functional teams.
Requirements
- Master’s student in technical field
- Foundational knowledge of machine learning
- Focus on geometric and spatial representation learning
- Hands-on experience with deep learning frameworks
- ML application in Structural Biology
- Excellent communication and collaboration skills
- Effective work in international, cross-functional teams
- Proficiency in English
- Strong verbal and written English skills
- Upload CV, motivation letter, enrolment certificate
- Mandatory university letter for non-EU/EFTA applicants
Tasks
- Generate large-scale synthetic biomolecular structure datasets
- Develop state-of-the-art multi-modal foundation models
- Define downstream tasks and evaluation protocols
- Collaborate with international, cross-functional teams
- Drive high-level research initiatives
- Steer results toward formal scientific publication
- Merge advanced computational techniques with drug discovery efforts
Education
- Currently in higher education
Languages
- English – Business Fluent
Tools & Technologies
- Machine Learning
- geometric and spatial representation learning
- deep learning frameworks
- PyTorch
- Structural Biology
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Roche and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
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RORoche
Intern in structure-based foundation modelling(m/w/x)
Basel
Full-timeInternshipOn-site
AI/ML
Nejo AI Summary
Generating large-scale synthetic biomolecular structure datasets for foundation models. Master’s student with ML and geometric representation learning experience required. Collaboration in international, cross-functional teams.
Requirements
- Master’s student in technical field
- Foundational knowledge of machine learning
- Focus on geometric and spatial representation learning
- Hands-on experience with deep learning frameworks
- ML application in Structural Biology
- Excellent communication and collaboration skills
- Effective work in international, cross-functional teams
- Proficiency in English
- Strong verbal and written English skills
- Upload CV, motivation letter, enrolment certificate
- Mandatory university letter for non-EU/EFTA applicants
Tasks
- Generate large-scale synthetic biomolecular structure datasets
- Develop state-of-the-art multi-modal foundation models
- Define downstream tasks and evaluation protocols
- Collaborate with international, cross-functional teams
- Drive high-level research initiatives
- Steer results toward formal scientific publication
- Merge advanced computational techniques with drug discovery efforts
Education
- Currently in higher education
Languages
- English – Business Fluent
Tools & Technologies
- Machine Learning
- geometric and spatial representation learning
- deep learning frameworks
- PyTorch
- Structural Biology
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Roche and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
Like this job?
BetaYour Career Agent finds similar jobs for you every day.
About the Company
Roche
Industry
Healthcare
Description
The company is dedicated to advancing science and ensuring access to healthcare for everyone.
Not a perfect match?
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